Transferring information retrieval (IR) models from a high-resource language
(typically English) to other languages in a zero-shot fashion has become a
widely adopted approach. In this work, we show that the effectiveness of
zero-shot rankers diminishes when queries and documents are present in
different languages. Motivated by this, we propose to train ranking models on
artificially code-switched data instead, which we generate by utilizing
bilingual lexicons. To this end, we experiment with lexicons induced from (1)
cross-lingual word embeddings and (2) parallel Wikipedia page titles. We use
the mMARCO dataset to extensively evaluate reranking models on 36 language
pairs spanning Monolingual IR (MoIR), Cross-lingual IR (CLIR), and Multilingual
IR (MLIR). Our results show that code-switching can yield consistent and
substantial gains of 5.1 MRR@10 in CLIR and 3.9 MRR@10 in MLIR, while
maintaining stable performance in MoIR. Encouragingly, the gains are especially
pronounced for distant languages (up to 2x absolute gain). We further show that
our approach is robust towards the ratio of code-switched tokens and also
extends to unseen languages. Our results demonstrate that training on
code-switched data is a cheap and effective way of generalizing zero-shot
rankers for cross-lingual and multilingual retrieval.

本研究探讨了如何将零 - shot 模型从高资源语言（一般是英语）迁移到其他语言，结果表明在不同语言的查询和文档中使用零 - shot 排名算法的有效性会降低。因此，我们提出利用双语词典生成人工混合语言的数据来训练排名模型，我们对从跨语言词嵌入和平行维基百科页面标题引导的词典进行了实验，最终在多语言、跨语言和单语言信息检索方面进行了评估。结果表明，使用代码切换可以在跨语言和多语言检索中带来一致且实质性的收益。